An analysis of the surface of an object is often utilized as a screening device to determine the acceptability of the object, to forestall damage to or destruction of the object, or as an aid in effecting repairs to the object once damage has occurred. This surface analysis most often entails the detection and analysis of anomalies of or within the surface.
For example, the detection and analysis of the surface of an egg may be used to determine the salability of the egg. The analysis may determine that the egg is suitable for retail sale, e.g., it is unlikely to break during normal handling and has a pleasing appearance. On the other hand, the analysis may determine that the egg is suitable for commercial sale, e.g., while it is unlikely to break during normal handling, it has lumps or other shell deformities, hence would not appeal to the retail buyer. Again, analysis may determine that the egg is suitable for processing only, e.g., it is likely to break in normal handling, hence is suitable for conversion to powdered eggs.
In another example, normal wear may cause the surface of a road to develop cracks, potholes, etc. An analysis of the surface of the road would assist maintenance engineers in determining where and what type of repairs are needed. In addition, data from the surface analysis may be used by design engineers to improve road design, thereby reducing the maintenance requirements of future roads.
One form of surface analysis currently in use is visual inspection. Unfortunately, good visual inspection requires both a trained inspector and a significant inspection time, each of which is expensive. Visual inspection is also subject to inattentiveness errors, in which operator fatigue and attention span are factors. For these and other reasons, visual inspection techniques are problematical at best.
A common solution to visual inspection is some form of automated inspection. In the egg-surface example above, the eggs may be rotated while an automated inspection system is used to detect and analyze anomalies upon and/or within their shells. Those eggs with strong, blemish-free shells would then be allocated for retail sales, those with strong marred shells for commercial sales, and those with weak shells for processing. For simple anomalies, such as cracks, utilizing existing anomaly-detection techniques works well.
In the road-surface example above, an automated system may include a vehicle traveling the road while capturing either a continuous image (e.g., videotape) or a series of overlapping images (e.g., photographs) of the road surface. The image(s) thus captured may then be analyzed, either later or in real time, and surface anomalies detected. These detected anomalies may then be analyzed, classed, and mapped, with the resultant map providing the requisite data to the maintenance and design engineers.
Unlike the egg, the road does not have a simple surface with few potential anomalies. For example, road-surface anomalies include line anomalies, area anomalies, and other anomalies.
Line anomalies (cracks) may be either dark (open cracks) or light (dirt-filled cracks). Either dark or light line anomalies include longitudinal cracks, transverse cracks, oblique cracks, fatigue (crosshatched) cracks, and block cracks.
Area anomalies may be either linear (sealed cracks) or block (P2, or patches and potholes). Linear-area anomalies include sealed longitudinal cracks, sealed transverse cracks, sealed oblique cracks, sealed fatigue cracks, and sealed block cracks. Block-area anomalies include both potholes and patches (sealed potholes).
Other anomalies include road edges, road seams, manhole covers, drainage grates, speed bumps, traffic markings, road debris, and anything else upon or in the surface of the road.
Existing automated road-surface anomaly-detection techniques normally utilize rectilinear line and edge detection. This allows the detection of dark-line and transitional anomalies. The dark-line detection techniques normally allow the detection of longitudinal dark-line anomalies, e.g., open cracks along the direction of travel, transverse dark-line anomalies, e.g., open cracks across the direction of travel, and block dark-line anomalies, e.g., open cracks both along and across the direction of travel and "breaking" the surface into blocks. The transitional detection techniques usually detect transitions in surface texture and/or color and indicate a thin line or "crack" at the transition boundary.
A problem exists when conventional techniques are used to detect oblique line anomalies, e.g., cracks running diagonally to the direction of travel. Oblique line anomalies usually have insufficient existence in the longitudinal and transverse domains at any given point to be detectable using conventional techniques.
A problem exists when conventional techniques are used to detect light-line anomalies, e.g., cracks filled with dirt, sand, etc. Light-line anomalies usually offer insufficient contrast to the road surface to be detectable using conventional techniques.
A problem exists when conventional techniques are used to detect linear-area anomalies, e.g., cracks that have previously been repaired and appear as wide lines or linear areas on the road. Transitional detection techniques usually cause linear-area anomalies, if detectable at all, to be erroneously detected as a pair of parallel thin-line anomalies.
A problem exists when conventional techniques are used to detect rough block area anomalies, e.g., potholes, etc., and smooth block-area anomalies, e.g., patches, etc. Transitional detection techniques usually cause rough and smooth block-area anomalies, if detectable at all, to be erroneously detected as block line anomalies.
A like problem exists when conventional techniques are used to detect edge anomalies, e.g., road surface edges, pavement ends, etc., and joint anomalies, e.g., concrete joins, etc. Transitional detection techniques usually cause edge and joint anomalies, if detectable at all, to be erroneously detected as longitudinal or transverse line anomalies.
A problem exists when conventional techniques are used to detect fatigue anomalies, e.g., crosshatch or fatigue cracks, etc. Due to the high-density cracking found in fatigue anomalies, they are often interpreted as rough-area anomalies. Therefore, transition detection techniques usually cause fatigue anomalies, if detectable at all, to be erroneously detected as block line anomalies.
What is needed, therefore, is an automated process consisting of techniques by which at least a preponderance of surface anomalies may be detected, and by which detected anomalies may be analyzed and correctly classed as to anomaly type.